Artificial intelligence has penetrated every aspect of our lives in recent years. It operates across a wide spectrum, from search engines to CRM systems, from content creation to logistics optimization. However, despite all this advancement, AI, especially when it comes to generative models, still produces a problem that surprises people: AI hallucinations.
A hallucination is when an AI generates information with absolute accuracy that doesn’t actually exist. The model speaks with a confident tone, but the content is inaccurate, distorted, or completely fabricated. This is precisely where one of the most critical problems facing brands, academia, governments, and users today emerges.
Artificial intelligence doesn’t actually “know”; it simply makes probabilistic predictions. Language models extract patterns from millions of data points and select the next most likely word. In this process:
- Incomplete data,
- Conflicting sources,
- Incorrect context,
- Overgeneralization,
- Misinterpretation of user request,
situations like this cause the model to produce a response detached from reality.
In academic literature, this is also called “confabulation”—a mechanism very similar to the false memory mechanism in the human brain.
The examples experienced by many global companies and public institutions today have become part of technological history. Three significant cases clearly demonstrate how serious the problem can be:
1) Google Bard’s James Webb Space Telescope Scandal
In Google’s Bard launch, a model claimed that NASA’s James Webb telescope had “imaged an extrasolar planet for the first time.”
This information was completely false. The result? Google’s market value eroded by $100 billion in a single day.
This was one of the most dramatic examples demonstrating the financial impact of hallucination.
2) Air Canada Chatbot Case
A passenger bought a ticket relying on false information provided by a chatbot. The airline claimed it was not liable for the bot’s “misspoken speech.”
A Canadian court, however, ruled that “a chatbot is also a company employee,” and awarded the company damages.
AI hallucination has exposed a company to legal liability for the first time.
3) Lawyer’s Fake Case Law Files
A US lawyer presented a court with references from ChatGPT. There’s even a website containing case studies related to this.
All the case decisions the model produced were fabricated.
The result? The lawyer was disciplined, and the US legal system imposed additional regulations on AI use.
The inclusion of hallucinations in AI is critical. Because in the increasingly digital world, data accuracy now means brand trust, customer loyalty, share value, and even legal liability.
A brand provides false information:
- Losses trust,
- Damages perception,
- Leaves itself at the mercy of competitors,
- Disrupts customer experience,
- Can be subject to regulations.
While saying “We use AI” today is an advantage, a misapplication of AI can destroy a brand’s reputation in just a few hours.
But What Should Brands Do?
This is our topic, and there are certain points every brand employee should be mindful of. However, the most important of all lies in one word: audit.
Artificial intelligence can cause problems, especially in many sectors and departments. In digital marketing, inaccurate metric analysis, fabricated SEO data, and flawed campaign results can negatively impact expert decisions. Inaccurate competitor analysis conducted by benchmarking can set a brand back significantly. E-commerce, fake product descriptions, inaccurate pricing suggestions, and even inaccurate product image and video content from brands that still view marketing as mere packaging can open doors to a completely different world. In healthcare, fabricated disease names and incorrect drug combinations can unfortunately lead to death. Even more serious problems can arise in an integrated system where, at least the information Google indexes, is written by experts, but AI can use any information due to its ability to distinguish between experts and non-experts. In finance, deviations in risk scores, incorrect investment advice, and, unfortunately, advice that can lead to fatal errors, especially for small investors, can be found. It’s possible to present legal precedents and legal provisions that don’t actually exist as if they do. It’s quite easy to multiply these examples.
So, the issue isn’t just technology—it’s business outcomes. Blindly using AI output is now risky. Here are the methods brands should implement:
Human + AI (Human-in-the-loop)
Machine-generated content must be reviewed by an expert. This is especially essential in fields such as healthcare, finance, law, public administration, and crisis communication.
Source Verification Layers
The model must verify the information it provides with a database, academic paper, or internally verified sources.
Security and Ethical Layers
Hallucination is sometimes more than just misinformation; it can lead to major risks such as:
- Personal data breach
- Incorrect medical advice
- Financial misdirection
That’s why brands are now managing their AI use with ethical policies, security protocols, and accuracy testing.
AI hallucinations can be much more than just a mistake in the increasingly digital world. This is reshaping brands’ data strategy, customer communication, content creation, and risk management.
Today’s marketer, manager, CEO, or entrepreneur must ask themselves this question:
“I’m harnessing the power of AI, but am I not abandoning reality control?”
The future belongs to artificial intelligence, but it’s still humans who will manage it—specifically, humans who can accurately interpret, query, and manage data. Those who saw the internet as a monster yesterday may see AI as one today. But I believe we humans, operating directly and without oversight, are the ones truly responsible for the problems facing us as humanity. Just as the internet revolution did, artificial intelligence will shape our lives, change our habits, and help us achieve a different future. However, even though AI is being developed far from oversight and control, we should be able to implement our own personal precautions, at least before international and legal protections are introduced.
